<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Healthcare-Analytics on Pi Stack</title>
    <link>https://www.pistack.xyz/tags/healthcare-analytics/</link>
    <description>Recent content in Healthcare-Analytics on Pi Stack</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Sun, 14 Jun 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://www.pistack.xyz/tags/healthcare-analytics/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Self-Hosted Survival Analysis: lifelines vs scikit-survival vs survival — Time-to-Event Modeling Tools Compared</title>
      <link>https://www.pistack.xyz/posts/2026-06-14-self-hosted-survival-analysis-lifelines-scikit-survival-survival-r/</link>
      <pubDate>Sun, 14 Jun 2026 00:00:00 +0000</pubDate>
      <guid>https://www.pistack.xyz/posts/2026-06-14-self-hosted-survival-analysis-lifelines-scikit-survival-survival-r/</guid>
      <description>&lt;p&gt;Survival analysis — also called time-to-event analysis — is the statistical framework for analyzing data where the outcome is the time until an event occurs. Originally developed for clinical trials (time to death, disease recurrence), survival analysis now powers churn prediction, equipment failure modeling, customer lifetime value estimation, and countless other applications. In this guide, we compare three open-source survival analysis libraries that you can self-host for reproducible biomedical and analytical research.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
